Generative AI in Finance: Forecasting, Fraud & Regulation Training Course

Foundations of Generative AI

• Understanding generative models and their strategic value in finance
• Key model types: LLMs, GANs, and VAEs
• Assessing strengths and limitations within financial applications

Leveraging Generative Adversarial Networks (GANs) in Finance

• Mechanism of GANs: the interplay between generators and discriminators
• Practical uses in creating synthetic data and simulating fraud scenarios
• Case study: Producing realistic transaction data for rigorous testing

Large Language Models (LLMs) and Advanced Prompting

• How LLMs process and generate specialized financial text
• Developing effective prompts for forecasting and risk assessment
• Real-world applications: Summarizing reports, KYC procedures, and identifying red flags

Enhancing Financial Forecasting with Generative AI

• Time series prediction using hybrid LLM and machine learning models
• Creating scenarios and conducting stress tests
• Use case: Revenue forecasting by integrating structured and unstructured data sources

Advanced Fraud Detection and Anomaly Identification

• Applying GANs to detect anomalies in transaction flows
• Uncovering emerging fraud patterns via LLM-driven prompt workflows
• Evaluating models: Distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in AI-generated outputs
• Mitigating risks related to model hallucinations and bias in financial contexts
• Aligning with regulatory standards such as GDPR and Basel guidelines

Developing Generative AI Solutions for Financial Institutions

• Constructing compelling business cases for internal adoption
• Achieving a balance between technological innovation, risk management, and compliance
• Establishing governance frameworks for responsible AI implementation

Recap and Future Roadmap